scholarly journals Bayesian approach to constraining the properties of ionized bubbles during reionization

2020 ◽  
Vol 496 (1) ◽  
pp. 739-753 ◽  
Author(s):  
Raghunath Ghara ◽  
T Roy Choudhury
Keyword(s):  
Bayesian Approach ◽  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Neutral Hydrogen ◽  
Bayesian Framework ◽  
Signal To Noise ◽  
Noise Ratio ◽  
The Difference ◽  
Bubble Sizes

ABSTRACT A possible way to study the reionization of cosmic hydrogen is by observing the large ionized regions (bubbles) around bright individual sources, e.g. quasars, using the redshifted 21 cm signal. It has already been shown that matched filter-based methods are not only able to detect the weak 21 cm signal from these bubbles but also aid in constraining their properties. In this work, we extend the previous studies to develop a rigorous Bayesian framework to explore the possibility of constraining the parameters that characterize the bubbles. To check the accuracy with which we can recover the bubble parameters, we apply our method on mock observations appropriate for the upcoming SKA1-low. For a region of size ≳50 cMpc around a typical quasar at redshift 7, we find that ≈20 h of integration with SKA1-low will be able to constrain the size and location of the bubbles, as well as the difference in the neutral hydrogen fraction inside and outside the bubble, with $\lesssim 10$ per cent precision. The recovery of the parameters are more precise and the signal-to-noise ratio of the detected signal is higher when the bubble sizes are larger and their shapes are close to spherical. Our method can be useful in identifying regions in the observed field that contain large ionized regions and hence are interesting for following up with deeper integration times.

scholarly journals Novel Configurations of Ultrahigh Frequency (≤600 MHz) Analog Frontend for High Resolution Ultrasound Measurement

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2598
Author(s):  
Min Kim ◽  
Jinhyoung Park ◽  
Qifa Zhou ◽  
Koping Shung
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Ultrahigh Frequency ◽  
Ultrasound Measurement ◽  
Functional Responses ◽  
Signal To Noise ◽  
Coded Excitation ◽  
Noise Ratio ◽  
The Difference

In this article, an approach to designing and developing an ultrahigh frequency (≤600 MHz) ultrasound analog frontend with Golay coded excitation sequence for high resolution imaging applications is presented. For the purpose of visualizing specific structures or measuring functional responses of micron-sized biological samples, a higher frequency ultrasound is needed to obtain a decent spatial resolution while it lowers the signal-to-noise ratio, the difference in decibels between the signal level and the background noise level, due to the higher attenuation coefficient. In order to enhance the signal-to-noise ratio, conventional approach was to increase the transmit voltage level. However, it may cause damaging the extremely thin piezoelectric material in the ultrahigh frequency range. In this paper, we present a novel design of ultrahigh frequency (≤600 MHz) frontend system capable of performing pseudo Golay coded excitation by configuring four independently operating pulse generators in parallel and the consecutive delayed transmission from each channel. Compared with the conventional monocycle pulse approach, the signal-to-noise ratio of the proposed approach was improved by 7–9 dB without compromising the spatial resolution. The measured axial and lateral resolutions of wire targets were 16.4 µm and 10.6 µm by using 156 MHz 4 bit pseudo Golay coded excitation, respectively and 4.5 µm and 7.7 µm by using 312 MHz 4 bit pseudo Golay coded excitation, respectively.

Accurate detection of low signal-to-noise ratio neuronal calcium transient waves using a matched filter

2016 ◽  
Vol 259 ◽  
pp. 1-12 ◽  
Author(s):  
Agnieszka F. Szymanska ◽  
Chiaki Kobayashi ◽  
Hiroaki Norimoto ◽  
Tomoe Ishikawa ◽  
Yuji Ikegaya ◽  
...  
Keyword(s):  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Calcium Transient ◽  
Signal To Noise ◽  
Transient Waves ◽  
Accurate Detection ◽  
Noise Ratio

A Bayesian Multiresolution Approach for Noise Removal in Medical Magnetic Resonance Images

2018 ◽  
Vol 29 (1) ◽  
pp. 189-201 ◽  
Author(s):  
Sima Sahu ◽  
Harsh Vikram Singh ◽  
Basant Kumar ◽  
Amit Kumar Singh
Keyword(s):  
Magnetic Resonance ◽  
Bayesian Approach ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
White Gaussian Noise ◽  
Error Estimator ◽  
Wavelet Coefficients ◽  
Signal To Noise ◽  
Noise Ratio

Abstract A Bayesian approach using wavelet coefficient modeling is proposed for de-noising additive white Gaussian noise in medical magnetic resonance imaging (MRI). In a parallel acquisition process, the magnetic resonance image is affected by white Gaussian noise, which is additive in nature. A normal inverse Gaussian probability distribution function is taken for modeling the wavelet coefficients. A Bayesian approach is implemented for filtering the noisy wavelet coefficients. The maximum likelihood estimator and median absolute deviation estimator are used to find the signal parameters, signal variances, and noise variances of the distribution. The minimum mean square error estimator is used for estimating the true wavelet coefficients. The proposed method is simulated on MRI. Performance and image quality parameters show that the proposed method has the capability to reduce the noise more effectively than other state-of-the-art methods. The proposed method provides 8.83%, 2.02%, 6.61%, and 30.74% improvement in peak signal-to-noise ratio, structure similarity index, Pratt’s figure of merit, and Bhattacharyya coefficient, respectively, over existing well-accepted methods. The effectiveness of the proposed method is evaluated by using the mean squared difference (MSD) parameter. MSD shows the degree of dissimilarity and is 0.000324 for the proposed method, which is less than that of the other existing methods and proves the effectiveness of the proposed method. Experimental results show that the proposed method is capable of achieving better signal-to-noise ratio performance than other tested de-noising methods.

scholarly journals The robustness of the differential quantizer in the case of the variable signal to noise ratio

2017 ◽  
Vol 14 (1) ◽  
pp. 149-160
Author(s):  
Lazar Cokic ◽  
Aleksandra Marjanovic ◽  
Sanja Vujnovic ◽  
Zeljko Djurovic
Keyword(s):  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Fixed Number ◽  
Signal To Noise ◽  
Input And Output ◽  
Noise Ratio ◽  
The Difference ◽  
Optimal Signal ◽  
Theoretical Overview

In this paper a short theoretical overview of differential quantizer and its implementations is given. Afterward, the effect of the order of prediction in differential quantizer and the effect of the difference in order of predictor in the input and output of differential quantizer is analyzed. Then it was proceeded with the examination of the robustness of the differential quantizer in the case in which a noise signal is brought to the input of the differential quantizer, instead of the clean speech signal. The analysis was conducted with a uniform distribution, as well as the noise with the gaussian distribution, and the obtained results were adequately commented on. Also, experimentally a limit was set which refers to the intensity of the noise and still enable results which are better that a regular uniform quantizer. The whole analysis is done by using the fixed number of bits in quantization, i.e. 12-bit quantizer is used in all the implementations of differential quantizer. In the conclusion of this paper there is a discussion about the possibility of implementing a differential quantizer which will be able to recognize which noise attacks the system, and in addition to that, in what form it adapts its coefficients so that it at any moment acquires the optimal signal to noise ratio.

Resolution performance of Wiener filters

Geophysics ◽  
1983 ◽  
Vol 48 (7) ◽  
pp. 887-899 ◽  
Author(s):  
S. H. Bickel ◽  
D. R. Martinez
Keyword(s):  
Time Constant ◽  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Resolving Power ◽  
General Definition ◽  
Resolution Time ◽  
Signal To Noise ◽  
Temporal Separation ◽  
Noise Ratio

To improve the resolution of seismic events, one often designs a Wiener inverse filter that optimally (in the least‐squares sense) transforms a measured source signature into a spike. When this filter is applied to seismic data, the bandwidth of any noise which is present increases along with the bandwidth of the signal. Thus the signal‐to‐noise ratio is degraded. To reduce signal ambiguity it is common practice to prewhiten the Wiener filter. Prewhitening the filter improves the output signal‐to‐ambient noise ratio, but at the same time it reduces resolution. The ability to resolve the temporal separation between events is determined by the resolution time constant which we define as the ratio of signal energy to peak signal power from the filter. For unfiltered wavelets the resolution time constant becomes the reciprocal of resolving power recently described by Widess (1982). For matched filter signals the resolution time constant can be regarded as the inverse of the frequency span of the signal. Although it is satisfying that the resolution time constant definition agrees with other measures of resolution, this more general definition has two major advantages. First, it incorporates the effect of filtering; second, it is easily generalized to incorporate the effects of noise by assuming that the filter is a Wiener filter. For a given amount of noise the Wiener filter is a generalization of the matched filter. Marine seismic wavelets demonstrate how reducing the noise level improves the resolution of a Wiener filter relative to a matched filter. For these wavelets a point of diminishing return is reached, such that, to realize a further small increase in resolution, a large increase in input signal‐to‐noise ratio is required to maintain interpretable information at the output.

scholarly journals Confidence Intervals for the Signal-to-Noise Ratio and Difference of Signal-to-Noise Ratios of Log-Normal Distributions

Stats ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 164-173 ◽  
Author(s):  
Warisa Thangjai ◽  
Sa-Aat Niwitpong
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Coverage Probability ◽  
Average Length ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Normal Distributions ◽  
Noise Ratio ◽  
The Difference ◽  
Log Normal

In this article, we propose approaches for constructing confidence intervals for the single signal-to-noise ratio (SNR) of a log-normal distribution and the difference in the SNRs of two log-normal distributions. The performances of all of the approaches were compared, in terms of the coverage probability and average length, using Monte Carlo simulations for varying values of the SNRs and sample sizes. The simulation studies demonstrate that the generalized confidence interval (GCI) approach performed well, in terms of coverage probability and average length. As a result, the GCI approach is recommended for the confidence interval estimation for the SNR and the difference in SNRs of two log-normal distributions.

scholarly journals Prospects for cosmic magnification measurements using H i intensity mapping

2020 ◽  
Vol 496 (2) ◽  
pp. 1959-1966
Author(s):  
Amadeus Witzemann ◽  
Alkistis Pourtsidou ◽  
Mario G Santos
Keyword(s):  
Signal To Noise Ratio ◽  
Noise Analysis ◽  
Neutral Hydrogen ◽  
Signal To Noise ◽  
Redshift Range ◽  
Intensity Mapping ◽  
Noise Ratio ◽  
Magnification Effect

ABSTRACT We investigate the prospects of measuring the cosmic magnification effect by cross-correlating neutral hydrogen intensity mapping (H i IM) maps with background optical galaxies. We forecast the signal-to-noise ratio for H i IM data from SKA1-MID and HIRAX, combined with LSST photometric galaxy samples. We find that, thanks to their different resolutions, SKA1-MID and HIRAX are highly complementary in such an analysis. We predict that SKA1-MID can achieve a detection with a signal-to-noise ratio of ∼15 on a multipole range of ℓ ≲ 200, while HIRAX can reach a signal-to-noise ratio of ∼50 on 200 < ℓ < 2000. We conclude that measurements of the cosmic magnification signal will be possible on a wide redshift range with foreground H i intensity maps up to z ≲ 2, while optimal results are obtained when 0.6 ≲ z ≲ 1.3. Finally, we perform a signal to noise analysis that shows how these measurements can constrain the H i parameters across a wide redshift range.

Maximization of signal-to-noise ratio in optical coherence tomography using a depth-dependent matched filter

2012 ◽  
Author(s):  
Ameneh Boroomand ◽  
Michael S. D. Smith ◽  
Dan P. Popescu ◽  
Michael Sowa ◽  
Sherif S. Sherif
Keyword(s):  
Optical Coherence Tomography ◽  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Optical Coherence ◽  
Signal To Noise ◽  
Noise Ratio
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